DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 12 is directed to a “computer-readable storage medium”. However, the claim is not limited to nontransitory embodiments, and the specification does not provide a definition limiting the meaning of this term to only nontransitory embodiments (see [0090]). The claim therefore can be reasonably interpreted as encompassing transitory signal embodiments, which are nonstatutory (In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007)). If the specification includes written description support, this rejection can be overcome by including the term “nontransitory” in the claim (see USPTO Official Gazette notice 1351 OG 212.).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Marisetty et al. (Advancing Smart Malnutrition Monitoring: A Multi-Modal Learning Approach for Vital Health Parameter Estimation, 31 July 2023, arXiv, Pages 1-10), hereinafter “Marisetty”.
Regarding claim 1, Marisetty teaches:
A subject body index estimation method for medical imaging (See the Abstract.), characterized in that the method comprises:
obtaining image data of a subject captured by an image capture apparatus (See page 6, right column, section B: “An RGB image of a frontal pose of person standing at a distance of 1.5 meters from the camera lens placed 1 meter from the ground is captured under sufficient lighting conditions as depicted in Fig. 10 (a).”);
extracting a first feature vector (See the unimodal representation extraction of the person/body mask in Fig. 5.) and key point information at a predetermined position of the body of the subject from the image data (See page 3, right column, section III.A.1): “To extract the face crop from full body image we perform face verification, cropping and subsequently alignment. The initial step of face detection determines the position of a face, by traversing through the points around the facial region to locate 68 landmarks.”);
generating a second feature vector based on the key point information (See the unimodal representation extraction of the face in Fig. 5.); and
estimating at least one of the height and the weight of the subject based on the first feature vector and the second feature vector (See in Fig. 5, “Predicted Weight” based on the multimodal representation fusion of the face, person/body mask, and 3D reconstruction representations.).
Regarding claim 2, Marisetty teaches:
The method according to claim 1, wherein the first feature vector represents at least one feature among an edge, texture, a shape, and a color in the image data (See in Fig. 4, the mask and mesh of the body, which meet the claimed “shape”.).
Regarding claim 3, Marisetty teaches:
The method according to claim 1, wherein the second feature vector represents a distance between anatomical structures of the subject or reflects a proportional relationship of distances between anatomical structures of the subject (See page 3, right column, section III.A.1): “Subsequently, the faces are aligned and transformed such that facial landmarks (inner eyes and bottom lip) appear in approximately in same regions, preserving the collinearity, parallelism, and the ratio of distances between the points with Affine Transformation.”).
Regarding claim 4, Marisetty teaches:
The method according to claim 1, wherein the image data comprises two-dimensional optical image data (See page 6, right column, section B: “An RGB image of a frontal pose of person standing at a distance of 1.5 meters from the camera lens placed 1 meter from the ground is captured under sufficient lighting conditions as depicted in Fig. 10 (a).”) and depth image data (See page 4, left column, III.A.3): “For an orthogonal projected 2D point given by π(X) = x = (Xx,Xy), an image feature embedding is extracted by function f. Then the occupancy of the query 3D point X is estimated by Eq. 3 where Z = Xz is the depth along the ray defined by the 2D projection x.”).
Regarding claim 5, Marisetty teaches:
The method according to claim 1, characterized in that extracting the first feature vector and the key point information at the predetermined position of the body of the subject from the image data comprises: extracting the first feature vector from the image data by using a convolutional neural network (See Xception, which is a convolutional neural network, in Fig. 5.); and extracting the key point information at the predetermined position of the body of the subject from the image data by using a human body posture estimation model (See VGG-Face in Fig. 5.).
Regarding claim 6, Marisetty teaches:
The method according to claim 1, characterized in that the predetermined position comprises: at least two of a head top, a shoulder, a nose, an eye, an ear, an arm, an elbow, a wrist, a hip, a knee, and an ankle (See the 68 landmarks including nose, eyes, and ears on page 3, section III.A.1).).
Regarding claim 7, Marisetty teaches:
The method according to claim 1, characterized in that generating the second feature vector based on the key point information comprises: calculating distances between at least two pairs of key points based on the key point information; and generating the second feature vector based on the distances (See page 3, right column, section III.A.1): “Subsequently, the faces are aligned and transformed such that facial landmarks (inner eyes and bottom lip) appear in approximately in same regions, preserving the collinearity, parallelism, and the ratio of distances between the points with Affine Transformation.”).
Regarding claim 8, Marisetty teaches:
The method according to claim 7, characterized in that each pair of key points comprises two key points in the length direction or two key points in the width direction of a human body (See page 3, right column, section III.A.1): “Subsequently, the faces are aligned and transformed such that facial landmarks (inner eyes and bottom lip) appear in approximately in same regions, preserving the collinearity, parallelism, and the ratio of distances between the points with Affine Transformation.” Parallelism of landmarks from the inner eyes and bottom lip meet the claimed “pair of key points” and “two key points in the length direction”.).
Regarding claim 9, Marisetty teaches:
The method according to claim 1, characterized in that estimating at least one of the height and the weight of the subject based on the first feature vector and the second feature vector comprises: concatenating the first feature vector and the second feature vector to generate an input feature vector; and inputting the input feature vector into one or two regressors to obtain at least one of the height and the weight of the subject (See the multimodal representation fusion and regression in Fig. 5.).
Regarding claim 10, Marisetty teaches:
The method according to claim 1, characterized in that estimating at least one of the height and the weight of the subject based on the first feature vector and the second feature vector comprises: generating a third feature vector based on inherent information of the subject (See Fig. 5 and its caption. The representations are concatenated with gender information.); concatenating the first feature vector, the second feature vector, and the third feature vector to generate an input feature vector (See the multimodal representation fusion of all modalities in Fig. 5.); and inputting the input feature vector into one or two regressors to obtain at least one of the height and the weight of the subject (See the regression in Fig. 5.).
Regarding claim 11, Marisetty teaches:
The method according to claim 10, characterized in that the inherent information comprises at least one of age and gender (See gender in Fig. 5.).
Regarding claim 12, Marisetty teaches:
A computer-readable storage medium, comprising a stored computer program, wherein the subject body index estimation method for medical imaging according to claim 1 is performed when the computer program is run (See the smartphone application in Fig. 2.).
Regarding claim 13, Marisetty teaches:
A medical imaging system, characterized in that the system comprises: an image capture apparatus, capturing image data of a subject; and a controller, connected to the image capture apparatus and used to perform the subject body index estimation method according to claim 1 (See the smartphone application in Fig. 2.).
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN S LEE whose telephone number is (571)272-1981. The examiner can normally be reached 11:30 AM - 7:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached at (571)270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jonathan S Lee/Primary Examiner, Art Unit 2677
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